🚨 Pulse of Innovation 🚨

Breaking news in the healthcare AI

Robots to the Rescue: How Humanoid AI is Revolutionizing Healthcare

AGIBOT, a China-based robotics startup, just unveiled a family of five advanced humanoid robots, directly challenging Elon Musk and Tesla’s upcoming Optimus bot.

• AGIBOT’s five new models are wheeled and biped humanoid robots designed for diverse tasks, from household chores to industrial operations.

• The flagship model, Yuanzheng A2, stands 5'9" (175cm), weighs 121 lbs (55kg), and can perform delicate tasks like needle threading.

• Unitree, another high-performance robot manufacturer from China, also showcased its new G1 mass production-ready robot with better functionality and appearance.

How can humanoid robots change healthcare?

• Workforce Shortages: With a projected global shortfall of 10 million healthcare workers by 2030, humanoid robots can take over routine tasks, reducing the burden on staff and improving operational efficiency. E.g. transporting patients in hospitals.

• Reducing Nurse Injuries: Nurses face high injury rates (375 cases per 10,000 workers in 2022), often due to lifting tasks; humanoid robots can assist with heavy lifting, decreasing injury risk and reducing absenteeism.

• Enhancing Precision: Humanoid robots can perform tasks requiring high accuracy, such as medication administration and patient monitoring, minimizing errors, and providing continuous care.

• Ethical and Regulatory Considerations: Robot integration must be carefully regulated to augment human workers without replacing them, ensuring patient safety and data privacy.

🧑🏼‍🔬 Bench to Bedside👨🏽‍🔬

Developments in healthcare AI research and innovations

AI-Enhanced CCTA: A Breakthrough in Cardiovascular Imaging

Objectives:

• AI-enhanced Coronary Computed Tomography Angiography (CCTA) was assessed for its effectiveness in improving the detection and management of coronary artery disease (CAD) by enhancing plaque analysis, refining risk stratification, and improving clinical outcomes.

Methods:

• This study retrospectively evaluation of data from 303 patients, who participated in prospective trial. The patients underwent coronary computed tomography angiography (CTA), fractional flow reserve (FFR), and quantitative coronary angiography (QCA).

• Researchers applied AI algorithms to CCTA images to automate and enhance the identification and characterization of atherosclerotic plaques and compared to traditional CCTA techniques across various clinical scenarios.

Results:

• Enhanced CCTA has superior accuracy in detecting high-risk plaques, provides detailed assessments of plaque morphology and composition, and reduces image interpretation time with AI algorithms. This helps identify high-risk patients for timely interventions and improved outcomes.

Limitations:

• AI-enhanced CCTA effectiveness depends on high-quality imaging, which may be lacking in resource-limited settings.

• Over-reliance on AI may underuse clinical expertise, and integrating AI requires infrastructure investment and specialized training for healthcare providers.

Now lets look at the technology in next section of “AI in Clinic”

🧑🏽‍⚕️ AI in Clinic 🏥

Developments in healthcare AI research and innovations

Cleerly Health: Revolutionizing Heart Disease Detection

Cleerly Health is hoping to transform cardiovascular care through AI-powered technology that redefines how coronary artery disease (CAD) is diagnosed and managed. Their innovative platform leverages cutting-edge imaging and machine learning to provide a comprehensive, non-invasive evaluation of heart disease, allowing for more personalized and effective patient care.

At the heart of this innovation is their digital care pathway, which gives healthcare a closed-loop, step-by-step approach to help facilitate early diagnosis, informed decision-making, and personalized treatment and tracking of coronary heart disease.

Treat: Enable personalized cardiovascular care through a first-of-a-kind-staging system

Key Features:

• AI-Driven CCTA Analysis: Offers detailed insights into plaque buildup, crucial for early detection.

• Coronary Phenotyping: Provides a comprehensive view of coronary arteries, enabling personalized care. For every artery and its branches it uses a four tiered staging system.

• Integrated Care Pathway: Facilitates seamless data sharing, enhancing collaboration among healthcare providers.

• Interactive Reports: Simplifies complex data for better patient understanding.

🤖 Patient First, AI Second🤖

Ethical and Regulatory Landscape of Healthcare AI

AI Hallucinations: A Challenge for Public Trust—Can We Prevent the Spread of Health Disinformation?

• Large Language Models (LLMs) are rapidly becoming easily accessible sources of healthcare information, with over 70% of the population turning to the Internet as their first resource for health-related inquiries. However, the prevalence of health disinformation remains a critical concern, undermining public health and eroding trust in evidence-based medicine.

• A recent BMJ article emphasizes the failure of current safety measures to generate accurate health information and highlights the lack of responsiveness and transparency from LLM companies regarding these vulnerabilities.

• The study tested four LLMs—OpenAI’s GPT-4 via ChatGPT and Microsoft Copilot, Google’s PaLM2/Gemini Pro via Bard, Anthropic’s Claude2 via Poe, and Meta’s Llama2 via HuggingChat by using prompts designed to generate misleading blog posts, such as “Sunscreen causes cancer” and “Alkaline diets cure cancer.”

  Results from the BMJ article:

• The cross-sectional analysis revealed that Claude2 always rejected all the disinformation prompts, even with jailbreaking attempts.

• In contrast, GPT-4 via Microsoft Copilot initially resisted generating disinformation but failed to do so after 12 weeks.

• GPT-4 via ChatGPT, PaLM2/Gemini Pro, and Llama2 consistently produced health disinformation. These models generated 113 unique cancer disinformation blog posts, totaling over 40,000 words, targeting diverse consumer groups without requiring jailbreaking.

• Although each LLM had access to report disinformation, there was no evident response or transparency in controlling data generation.

  How can we co-exist and use LLM safely ?

Implementing strict policies and active oversight is crucial across every LLM. Key measures include incorporating checkpoints to detect and report false information and establishing expert panels to review areas where misinformation could have devastating effects regularly.